Abstract

Purpose

Online social networks (OSNs) are now among the most popular applications on the web
offering platforms for people to interact, communicate and collaborate with others.
The rapid development of OSNs provides opportunities for people’s daily communication,
but also brings problems such as burst network traffic and overload of servers. Studying
the population growth pattern in online social networks helps service providers to
understand the people communication manners in OSNs and facilitate the management
of network resources. In this paper, we propose a population growth model for OSNs
based on the study of population distribution and growth in spatiotemporal scale-space.

Methods

We investigate the population growth in three data sets which are randomly sampled
from the popular OSN web sites including Renren, Twitter and Gowalla. We find out
that the number of population follows the power-law distribution over different geographic
locations, and the population growth of a location fits a power function of time.
An aggregated population growth model is conducted by integrating the population growth
over geographic locations and time.

Results

We use the data sets to validate our population growth model. Extensive experiments
also show that the proposed model fits the population growth of Facebook and Sina
Weibo well. As an application, we use the model to predict the monthly population
in three data sets. By comparing the predicted population with ground-truth values,
the results show that our model can achieve a prediction accuracy between 86.14% and 99.89%.

Conclusions

With our proposed population growth model, people can estimate the population size
of an online social network in a certain time period and it can also be used for population
prediction for a future time.

Keywords:

Spatiotemporal scale-space; Population distribution; Population growth; Online social
networks